Computational processes are widely-used to analyze, understand, integrate, and transform data. For example, a data mining process may be used to create highly-accurate, predictive, and descriptive models based on analysis of large amounts of data captured in a variety of industries to solve technical problems such as electrical and/or mechanical control system optimization, weather prediction, etc. Analytical tools train a statistical or machine learning model using the data to reliably predict an outcome, describe an optimum value for a control, determine an expected result, etc. After training, the trained model predicts or describes an outcome from new data. However, using the trained model with the identical data input to train the model may generate a different result, for example, as a result of differences in precision between the trained model executing in memory and the trained model executing after being stored in a file.